scholarly journals Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6489
Author(s):  
Joanna Kossakowska ◽  
Sebastian Bombiński ◽  
Krzysztof Ejsmont
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Signal Analysis ◽  
Cutting Tool ◽  
Large Set ◽  
Signal Features ◽  
Signal Characteristics ◽  
Basic Set ◽  
New Generation ◽  
The Relationship

There are many items in the literature indicating that certain signal features (SFs) of cutting forces, vibrations or acoustic emission are useful for the diagnosis of tool wear in certain single experiments. There is no answer to whether these SFs are universal. The novelty of this article is an attempt to answer these questions and propose a large set of SFs related to tool wear, but without including superfluous SFs. The analysis of the usefulness of the signal properties for the state of the cutting tool in turning was carried out on a large experiment. A number of various SFs obtained for various signal analysis methods were selected for the study. It is found that no SF is always related to the tool wear, so we define many different signal characteristics that can be related to the tool wear (basic set) and automatically select those associated with it in a given machining case. To this end, the relationship between the measures and the wear of the tool was analyzed. Interrelated measures were excluded from it. The obtained results can be used to build a new generation of more effective tool wear diagnostics systems. One of the goals of the tool wear diagnosis system is to save the energy used. The results can also enable the refinement of existing algorithms that predict the energy consumption of a machine.

scholarly journals Method of Controlling Cutting Tool Wear Based on Signal Analysis of Acoustic Emission for Milling

2017 ◽  
Vol 176 ◽  
pp. 246-252 ◽  
Author(s):  
Vadim A. Pechenin ◽  
Alexander I. Khaimovich ◽  
Alexsandr I. Kondratiev ◽  
Michael A. Bolotov
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Signal Analysis ◽  
Cutting Tool ◽  
Cutting Tool Wear

In-Process Tool Fracture Detection

1984 ◽  
Vol 106 (2) ◽  
pp. 111-118 ◽  
Author(s):  
M. S. Lan ◽  
D. A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Fracture Surface ◽  
Signal Analysis ◽  
Cutting Tool ◽  
Fracture Detection ◽  
Acoustic Emission Energy ◽  
Emission Energy ◽  
Ae Signal ◽  
The Relationship ◽  
Carbide Insert

This paper investigates the feasibility of using acoustic emission (AE) signal analysis for the detection of the breakage as well as the chipping of a cutting tool during machining. Experiments were conducted on a lathe using conventional carbide insert tools under realistic cutting conditions. The tangential and feed forces were also measured for comparisons. The sensitivities of the AE signal and cutting forces to insert chipping and fracture are illustrated. The relationship between acoustic emission energy and the combined effect of fracture surface area and cutting load is discussed.

Tool Wear Detection Using Time Series Analysis of Acoustic Emission

1989 ◽  
Vol 111 (3) ◽  
pp. 199-205 ◽  
Author(s):  
S. Y. Liang ◽  
D. A. Dornfeld
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Time Series Analysis ◽  
Cutting Tool ◽  
Shear Zones ◽  
Parameter Vector ◽  
Model Parameter ◽  
Cutting Tool Wear ◽  
Series Analysis

This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.

scholarly journals Internal Leakage Predicition of Hydraulic Spool valves Based on Acoustic Emission Technology

2021 ◽  
Vol 2113 (1) ◽  
pp. 012016
Author(s):  
Fei Song ◽  
Likun Peng ◽  
Jia Chen ◽  
Benmeng Wang
Keyword(s):  
Acoustic Emission ◽  
High Frequency ◽  
Peak Frequency ◽  
Leakage Rate ◽  
High Frequency Band ◽  
Signal Characteristics ◽  
Spool Valves ◽  
The Relationship ◽  
Ae Signals ◽  
Acoustic Emission Technology

Abstract In order to realize the nondestructive testing (NDT) of the internal leakage fault of hydraulic spool valves, the internal leakage rate must be predicted by AE (acoustic emission) technology. An AE experimental platform of internal leakage of hydraulic spool valves is built to study the characteristics of AE signals of internal leakage and the relationship between AE signals and leakage rates. The research results show the AE signals present a wideband characteristic. The main frequencies are concentrated in 30~50 kHz and the peak frequency is around 40 kHz. When the leakage rate is large, there are significant signal characteristics appearing in the high frequency band of 75~100 kHz. The exponent of the root mean square(RMS) of AE signals is positively correlated with the exponent of the leakage rate only if the leakage rate is greater than 2~3 mL/min. This find could be used to predict the internal leakage rate of hydraulic spool valves.

scholarly journals Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms

2020 ◽  
Vol 14 (5-6) ◽  
pp. 693-705
Author(s):  
Tiziana Segreto ◽  
Doriana D’Addona ◽  
Roberto Teti
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Inconel 718 ◽  
Signal Analysis ◽  
Elevated Temperatures ◽  
Wavelet Packet ◽  
Research Work ◽  
High Strength ◽  
Signal Features ◽  
Tool Wear Estimation

AbstractIn the last years, hard-to-machine nickel-based alloys have been widely employed in the aerospace industry for their properties of high strength, excellent resistance to corrosion and oxidation, and long creep life at elevated temperatures. As the machinability of these materials is quite low due to high cutting forces, high temperature development and strong work hardening, during machining the cutting tool conditions tend to rapidly deteriorate. Thus, tool health monitoring systems are highly desired to improve tool life and increase productivity. This research work focuses on tool wear estimation during turning of Inconel 718 using wavelet packet transform (WPT) signal analysis and machine learning paradigms. A multiple sensor monitoring system, based on the detection of cutting force, acoustic emission and vibration acceleration signals, was employed during experimental turning trials. The detected sensor signals were subjected to WPT decomposition to extract diverse signal features. The most relevant features were then selected, using correlation measurements, in order to be utilized in artificial neural network based machine learning paradigms for tool wear estimation.

Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

2020 ◽  
pp. 095440542094912
Author(s):  
Mohamad Javad Anahid ◽  
Hoda Heydarnia ◽  
Seyed Ali Niknam ◽  
Hedayeh Mehmanparast
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Acoustic Emission Signal ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Emission Signal ◽  
Signal Parameters ◽  
Signal Features

It is known that adequate knowledge of the sensitivity of acoustic emission signal parameters to various experimental parameters is indispensable. According to the review of the literature, a lack of knowledge was noticeable concerning the behavior of acoustic emission parameters under a broad range of machining parameters. This becomes more visible in milling operations that include sophisticated chip formation morphology and significant interaction effects and directional pressures and forces. To remedy the aforementioned lack of knowledge, the effect of the variation of cutting parameters on the time and frequency features of acoustic emission signals, extracted and computed from the milling operation, needs to be investigated in a wide aspect. The objective of this study is to investigate the effects of cutting parameters including the feed rate, cutting speed, depth of cut, material properties, as well as cutting tool coating/insert nose radius on computed acoustic emission signals featured in the frequency domain. Similar studies on time-domain signal features were already conducted. To conduct appropriate signal processing and feature extraction, a signal segmentation and processing approach is proposed based on dividing the recorded acoustic emission signals into three sections with specific signal durations associated with cutting tool movement within the work part. To define the sensitive acoustic emission parameters to the variation of cutting parameters, advanced signal processing and statistical approaches were used. Despite the time features of acoustic emission signals, frequency domain acoustic emission parameters seem to be insensitive to the variation of cutting parameters. Moreover, cutting factors governing the effectiveness of acoustic emission signal parameters are hinted. Among these, the cutting speed and feed rate seem to have the most noticeable effects on the variation of time–frequency domain acoustic emission signal information, respectively. The outcomes of this work, along with recently completed works in the time domain, can be integrated into advanced classification and artificial intelligence approaches for numerous applications, including real-time machining process monitoring.

Acoustic Emission Signal Acquisition and Analysis on Tool Wear

2014 ◽  
Vol 621 ◽  
pp. 171-178
Author(s):  
Hui Yu Huang ◽  
Yang Hong
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Acoustic Emission Signal ◽  
Cutting Tool ◽  
Signal Acquisition ◽  
Monitoring Methods ◽  
Damage State ◽  
Emission Signal

In the field of machinery manufacture, broken state at the time of the cutting tool in cutting metal, recognition has always been a study is of great significance. Currently, for the state of tool wear and collapse edge damage identification method already has a mature experience. However the existing condition monitoring methods are often used in accuracy and convenience has limitations, this paper USES the acoustic emission technology, as a kind of integrated online test sys tem design lay the foundation. This paper aimed at the sensor in the wireless transmission module, the performance characteristics of tool condition monitoring system of the main structure was designed, and then by acoustic emission signal from the cutting tool in cutting process as the research object, studies the cutting tool characteristics of acoustic emission signal under different damage state, for the on-line monitoring system design and calibration to provide theoretical support.

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